mlsanityData to 7 Oct 2026

Block-wise Scrambled Image Recognition Using Adaptation Network

arXiv 2001.07761 ↗First posted 21 Jan 2020cs.CV cs.LG eess.IVcited by 27

Not on the map. Why?

Abstract (arXiv)

In this study, a perceptually hidden object-recognition method is investigated to generate secure images recognizable by humans but not machines. Hence, both the perceptual information hiding and the corresponding object recognition methods should be developed. Block-wise image scrambling is introduced to hide perceptual information from a third party. In addition, an adaptation network is proposed to recognize those scrambled images. Experimental comparisons conducted using CIFAR datasets demonstrated that the proposed adaptation network performed well in incorporating simple perceptual information hiding into DNN-based image classification.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 10; 2023: 5; 2024: 5; 2025: 1; 2026: 2.

27 papers.

Sort:

  1. 2022An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applicationscited by 29
  2. 2022Privacy-Preserving Image Classification Using Isotropic Networkcited by 17
  3. 2021A Protection Method of Trained CNN Model with Secret Key from Unauthorized Accesscited by 15
  4. 2021Piracy-Resistant DNN Watermarking by Block-Wise Image Transformation with Secret Keycited by 9
  5. 2022Privacy-Preserving Image Classification Using Vision Transformercited by 9
  6. 2021Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Modelscited by 4
  7. 2022On the Transferability of Adversarial Examples between Encrypted Modelscited by 4
  8. 2023Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learningcited by 4
  9. 2023Privacy-Preserving Remote Heart Rate Estimation from Facial Videoscited by 4
  10. 2022Access Control of Semantic Segmentation Models Using Encrypted Feature Mapscited by 3
  11. 2024Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformercited by 3
  12. 2021Multitask Identity-Aware Image Steganography via Minimax Optimizationcited by 2
15 more
  1. 2023Color-NeuraCrypt: Privacy-Preserving Color-Image Classification Using Extended Random Neural Networkscited by 2
  2. 2022Access Control with Encrypted Feature Maps for Object Detection Modelscited by 1
  3. 2023Human-imperceptible, Machine-recognizable Imagescited by 1
  4. 2023Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Imagescited by 1
  5. 2024Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learningcited by 1
  6. 2024ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classificationcited by 1
  7. 2025AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving Approachcited by 1
  8. 2022A Pixel-based Encryption Method for Privacy-Preserving Deep Learning Modelscited by 0
  9. 2022An Encryption Method of ConvMixer Models without Performance Degradationcited by 0
  10. 2022Privacy-Preserving Image Classification Using ConvMixer with Adaptive Permutation Matrixcited by 0
  11. 2022On the Adversarial Transferability of ConvMixer Modelscited by 0
  12. 2024Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attackcited by 0
  13. 2024Privacy-Preserving Vision Transformer Using Images Encrypted with Restricted Random Permutation Matricescited by 0
  14. 2026Privacy-Preserving Semantic Segmentation without Key Managementcited by 0
  15. 2026Beyond Classification: Task-Dependent Learnability under Privacy-Motivated Image Transformationscited by 0